{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYTORCH_ENABLE_MPS_FALLBACK=1\n"
     ]
    }
   ],
   "source": [
    "%set_env PYTORCH_ENABLE_MPS_FALLBACK=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "def generate_time_series(n_points=1000, base_value=100):\n",
    "    start_date = datetime(2024, 1, 1)\n",
    "    timestamps = [start_date + timedelta(hours=x) for x in range(n_points)]\n",
    "    \n",
    "    x = np.arange(n_points)\n",
    "    trend = np.zeros(n_points)\n",
    "    \n",
    "    changepoints = [200, 400, 700]\n",
    "    slopes = [0.05, -0.03, 0.03, 0.01]  # Different slopes for each segment\n",
    "    \n",
    "    current_pos = 0\n",
    "    for i, cp in enumerate(changepoints):\n",
    "        trend[current_pos:cp] = x[current_pos:cp] * slopes[i]\n",
    "        current_pos = cp\n",
    "    trend[current_pos:] = x[current_pos:] * slopes[-1]\n",
    "    \n",
    "    hours = np.arange(n_points) % 24\n",
    "    seasonality = 15 * np.sin(2 * np.pi * hours / 24) + \\\n",
    "                 5 * np.cos(4 * np.pi * hours / 24)  \n",
    "    \n",
    "    noise = np.random.normal(0, 2, n_points)\n",
    "    \n",
    "    values = base_value + trend + seasonality + noise\n",
    "    \n",
    "    df = pd.DataFrame({\n",
    "        'unique_id': 'series_001',\n",
    "        'ds': timestamps,\n",
    "        'y': values\n",
    "    })\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = generate_time_series()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(df['ds'], df['y'])\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Value')\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from neuralforecast.auto import AutoNBEATS\n",
    "from neuralforecast import NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbeats_config = {\n",
    "   \"max_steps\": 100,\n",
    "   \"input_size\": tune.choice([192, 384]),\n",
    "   \"basis\": tune.choice(['legendre', 'polynomial', 'changepoint', 'piecewise_linear', 'linear_hat', 'spline', 'chebyshev']),\n",
    "   \"n_basis\": 5,\n",
    "   \"random_seed\": tune.randint(1, 10),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray.tune.search.hyperopt import HyperOptSearch\n",
    "from neuralforecast.losses.pytorch import *\n",
    "\n",
    "model = AutoNBEATS(\n",
    "    h=96,\n",
    "    loss=MAE(),\n",
    "    config=nbeats_config,\n",
    "    search_alg=HyperOptSearch(),\n",
    "    backend='ray',\n",
    "    num_samples=20\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-11 15:50:13,039\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:18,252\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:23,121\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:28,184\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:33,283\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:38,708\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:44,491\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:50,101\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:50:55,325\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:00,161\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:05,280\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:10,177\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:15,309\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:20,305\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:25,378\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:30,614\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:36,364\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:41,335\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:46,351\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:51,301\tINFO tensorboardx.py:308 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-12-11 15:51:51,309\tINFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/Users/marcopeix/ray_results/_train_tune_2024-12-11_15-50-07' in 0.0070s.\n",
      "Seed set to 9\n",
      "GPU available: True (mps), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name         | Type          | Params | Mode \n",
      "-------------------------------------------------------\n",
      "0 | loss         | MAE           | 0      | eval \n",
      "1 | padder_train | ConstantPad1d | 0      | train\n",
      "2 | scaler       | TemporalNorm  | 0      | train\n",
      "3 | blocks       | ModuleList    | 3.1 M  | train\n",
      "-------------------------------------------------------\n",
      "3.0 M     Trainable params\n",
      "56.4 K    Non-trainable params\n",
      "3.1 M     Total params\n",
      "12.263    Total estimated model params size (MB)\n",
      "30        Modules in train mode\n",
      "1         Modules in eval mode\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                             "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
      "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
      "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 43.83it/s, v_num=2, train_loss_step=4.480, train_loss_epoch=4.480]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00, 41.37it/s, v_num=2, train_loss_step=4.480, train_loss_epoch=4.480]\n"
     ]
    }
   ],
   "source": [
    "nf = NeuralForecast(models=[model], freq='M')\n",
    "nf.fit(df=df, val_size=192)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>config/max_steps</th>\n",
       "      <th>config/input_size</th>\n",
       "      <th>config/basis</th>\n",
       "      <th>config/n_basis</th>\n",
       "      <th>config/random_seed</th>\n",
       "      <th>config/h</th>\n",
       "      <th>config/loss</th>\n",
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       "      <th>17</th>\n",
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       "      <td>5.403413</td>\n",
       "      <td>100</td>\n",
       "      <td>192</td>\n",
       "      <td>changepoint</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>96</td>\n",
       "      <td>MAE()</td>\n",
       "      <td>MAE()</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.351707</td>\n",
       "      <td>5.403413</td>\n",
       "      <td>100</td>\n",
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       "      <td>changepoint</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>96</td>\n",
       "      <td>MAE()</td>\n",
       "      <td>MAE()</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.376642</td>\n",
       "      <td>5.127730</td>\n",
       "      <td>100</td>\n",
       "      <td>192</td>\n",
       "      <td>spline</td>\n",
       "      <td>5</td>\n",
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       "      <td>96</td>\n",
       "      <td>MAE()</td>\n",
       "      <td>MAE()</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>5.018386</td>\n",
       "      <td>100</td>\n",
       "      <td>192</td>\n",
       "      <td>piecewise_linear</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>MAE()</td>\n",
       "      <td>MAE()</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.499355</td>\n",
       "      <td>3.935876</td>\n",
       "      <td>100</td>\n",
       "      <td>384</td>\n",
       "      <td>changepoint</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>MAE()</td>\n",
       "      <td>MAE()</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        loss  train_loss  config/max_steps  config/input_size  \\\n",
       "17  2.351707    5.403413               100                192   \n",
       "11  2.351707    5.403413               100                192   \n",
       "14  2.376642    5.127730               100                192   \n",
       "9   2.391335    5.018386               100                192   \n",
       "13  2.499355    3.935876               100                384   \n",
       "\n",
       "        config/basis  config/n_basis  config/random_seed  config/h  \\\n",
       "17       changepoint               5                   9        96   \n",
       "11       changepoint               5                   9        96   \n",
       "14            spline               5                   4        96   \n",
       "9   piecewise_linear               5                   2        96   \n",
       "13       changepoint               5                   1        96   \n",
       "\n",
       "   config/loss config/valid_loss  \n",
       "17       MAE()             MAE()  \n",
       "11       MAE()             MAE()  \n",
       "14       MAE()             MAE()  \n",
       "9        MAE()             MAE()  \n",
       "13       MAE()             MAE()  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = nf.models[0].results.get_dataframe()\n",
    "\n",
    "config_cols = [col for col in results.columns if col.startswith('config')]\n",
    "columns_to_keep = ['loss', 'train_loss'] + config_cols\n",
    "existing_columns = [col for col in columns_to_keep if col in results.columns]\n",
    "filtered_results = results[existing_columns]\n",
    "best_runs = filtered_results.sort_values('loss', ascending=True).head(5)\n",
    "best_runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that allowing the choose between different basis allows the model to better adapt to different datasets. In this case, we simulated a dataset with trend changepoints and so the \"changepoint basis\" was selected as the best basis for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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